Image created with Gemini. Image prompt: A 1983 Gothic rock-opera film still in a vast Victorian academy library at night, a giant glowing open book eclipsed by a black disc with blazing gold corona hovering above a sweeping marble staircase, ribbons of luminous pages and index cards swirling through heavy fog toward the light, a woman in a windblown white dress on the stairs with arms raised while robed adult male scholars with radiant eyes drift past holding scrolls, extreme backlit haze and bloom, 35mm grain, the words RAG rendered as a huge bold chrome serif early-80s movie title card with glowing white backlight, 16:9.
We’ve built Cohere Transcribe Arabic, the world’s most accurate open-source model for Arabic speech recognition. Available under Apache 2.0″
https://x.com/cohere/status/2074499759616729149
Most agentic retrieval demos assume clean, well-structured documents. Enterprise reality is often different, consisting of messy PDFs where critical information is buried across tables, figures, and complex layouts. That’s why we teamed up with @lancedb to explore how LiteParse”
https://x.com/llama_index/status/2074170470119752084
SpheRoPE: Zero-Shot Optimization-Free 360 Panorama Generation with Spherical RoPE” TL;DR: injects spherical positional embeddings into pretrained diffusion transformers to generate seamless 360° panoramas and videos without any training or optimization.”
https://x.com/Almorgand/status/2072721320350044316
// In-context Retrieval at Million-token Scale // Great study providing better understanding of retrieval at million-token scale. They run the first systematic study of in-context retrieval at the scales real retrievers face, million-token corpora and length generalization far”
https://x.com/dair_ai/status/2074117920133898707
A great source to understand or refresh Transformer architecture It explains how transformers process text token by token, using self-attention to build contextual representations Covers: – Token embeddings and positional encodings – The residual stream that carries”
https://x.com/TheTuringPost/status/2072876709284852116
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